Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the AAAI Conference on Artificial Intelligence |
| Publikationsstatus | Veröffentlicht - 2 Nov. 2017 |
| Extern publiziert | Ja |
Abstract
To achieve peak performance, it is often necessary to adjust the parameters of a given algorithm to the class of problem instances to be solved; this is known to be the case for popular solvers for a broad range of AI problems, including AI planning, propositional satisfiability (SAT) and answer set programming (ASP). To avoid tedious and often highly sub-optimal manual tuning of such parameters by means of ad-hoc methods, general-purpose algorithm configuration procedures can be used to automatically find performance-optimizing parameter settings. While impressive performance gains are often achieved in this manner, additional, potentially costly parameter importance analysis is required to gain insights into what parameter changes are most responsible for those improvements. Here, we show how the running time cost of ablation analysis, a wellknown general-purpose approach for assessing parameter importance, can be reduced substantially by using regression models of algorithm performance constructed from data collected during the configuration process. In our experiments, we demonstrate speed-up factors between 33 and 14 727 for ablation analysis on various configuration scenarios from AI planning, SAT, ASP and mixed integer programming (MIP).
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
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Proceedings of the AAAI Conference on Artificial Intelligence. 2017.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Efficient Parameter Importance Analysis via Ablation with Surrogates
AU - Biedenkapp, André
AU - Lindauer, Marius
AU - Eggensperger, Katharina
AU - Hutter, Frank
AU - Fawcett, Chris
AU - Hoos, Holger H.
N1 - Funding information: Acknowledgments K. Eggensperger, M. Lindauer and F. Hutter acknowledge funding by the DFG (German Research Foundation) under Emmy Noether grant HU 1900/2-1; K. Eggensperger also acknowledges funding by the State Graduate Funding Program of Baden-Württemberg. H. Hoos acknowledges funding through an NSERC Discovery Grant.
PY - 2017/11/2
Y1 - 2017/11/2
N2 - To achieve peak performance, it is often necessary to adjust the parameters of a given algorithm to the class of problem instances to be solved; this is known to be the case for popular solvers for a broad range of AI problems, including AI planning, propositional satisfiability (SAT) and answer set programming (ASP). To avoid tedious and often highly sub-optimal manual tuning of such parameters by means of ad-hoc methods, general-purpose algorithm configuration procedures can be used to automatically find performance-optimizing parameter settings. While impressive performance gains are often achieved in this manner, additional, potentially costly parameter importance analysis is required to gain insights into what parameter changes are most responsible for those improvements. Here, we show how the running time cost of ablation analysis, a wellknown general-purpose approach for assessing parameter importance, can be reduced substantially by using regression models of algorithm performance constructed from data collected during the configuration process. In our experiments, we demonstrate speed-up factors between 33 and 14 727 for ablation analysis on various configuration scenarios from AI planning, SAT, ASP and mixed integer programming (MIP).
AB - To achieve peak performance, it is often necessary to adjust the parameters of a given algorithm to the class of problem instances to be solved; this is known to be the case for popular solvers for a broad range of AI problems, including AI planning, propositional satisfiability (SAT) and answer set programming (ASP). To avoid tedious and often highly sub-optimal manual tuning of such parameters by means of ad-hoc methods, general-purpose algorithm configuration procedures can be used to automatically find performance-optimizing parameter settings. While impressive performance gains are often achieved in this manner, additional, potentially costly parameter importance analysis is required to gain insights into what parameter changes are most responsible for those improvements. Here, we show how the running time cost of ablation analysis, a wellknown general-purpose approach for assessing parameter importance, can be reduced substantially by using regression models of algorithm performance constructed from data collected during the configuration process. In our experiments, we demonstrate speed-up factors between 33 and 14 727 for ablation analysis on various configuration scenarios from AI planning, SAT, ASP and mixed integer programming (MIP).
UR - http://www.scopus.com/inward/record.url?scp=85026381970&partnerID=8YFLogxK
U2 - 10.1609/aaai.v31i1.10657
DO - 10.1609/aaai.v31i1.10657
M3 - Conference contribution
BT - Proceedings of the AAAI Conference on Artificial Intelligence
ER -